Doppler spectrum and performance of E-SDM systems in indoor time-varying MIMO channels

2007 ◽  
Author(s):  
Huu Phu Bui ◽  
Hiroshi Nishimoto ◽  
Toshihiko Nishimura ◽  
Yasutaka Ogawa ◽  
Takeo Ohgane
Keyword(s):  
Doppler Spectrum ◽  
Time Varying ◽  
Mimo Channels ◽  
And Performance

Adaptive Estimation of Angular Power Spectra for Time-Varying MIMO Channels

2021 ◽  
Author(s):  
Navneet Agrawal ◽  
Renato L. G. Cavalcante ◽  
Slawomir Stanczak
Keyword(s):  
Adaptive Estimation ◽  
Power Spectra ◽  
Time Varying ◽  
Mimo Channels

scholarly journals HEDGE FUNDS: RISK AND PERFORMANCE

2018 ◽  
Vol 06 (01) ◽  
pp. 1850003
Author(s):  
SANGHEON SHIN ◽  
JAN SMOLARSKI ◽  
GÖKÇE SOYDEMIR
Keyword(s):  
Risk Factors ◽  
Hedge Funds ◽  
Factor Model ◽  
Explanatory Power ◽  
Hedge Fund ◽  
Investment Strategy ◽  
Time Varying ◽  
Asset Class ◽  
And Performance ◽  
Fund Portfolio

This paper models hedge fund exposure to risk factors and examines time-varying performance of hedge funds. From existing models such as asset-based style (ABS)-factor model, standard asset class (SAC)-factor model, and four-factor model, we extract the best six factors for each hedge fund portfolio by investment strategy. Then, we find combinations of risk factors that explain most of the variance in performance of each hedge fund portfolio based on investment strategy. The results show instability of coefficients in the performance attribution regression. Incorporating a time-varying factor exposure feature would be the best way to measure hedge fund performance. Furthermore, the optimal models with fewer factors exhibit greater explanatory power than existing models. Using rolling regressions, our customized investment strategy model shows how hedge funds are sensitive to risk factors according to market conditions.

scholarly journals Doppler spectrum of microwave SAR signals from two-dimensional time-varying sea surface

2016 ◽  
Vol 30 (10) ◽  
pp. 1265-1276 ◽  
Author(s):  
Yunhua Wang ◽  
Yanmin Zhang ◽  
Huimin Li ◽  
Ge Chen
Keyword(s):  
Sea Surface ◽  
Doppler Spectrum ◽  
Time Varying ◽  
Two Dimensional

scholarly journals Linkages and performance comparison among Eastern Europe stock markets

2014 ◽  
Author(s):  
José Soares Da Fonseca
Keyword(s):  
Stock Markets ◽  
Performance Comparison ◽  
Market Model ◽  
Var Model ◽  
Time Varying ◽  
Comparative Performance ◽  
Regional Market ◽  
Auto Regressive ◽  
And Performance ◽  
The Empirical Analysis

This article studies the linkages among the stock markets of Bulgaria, Czech Republic, Estonia, Hungary, Poland, Romania, Russia, Serbia, Slovenia and Ukraine. The empirical analysis begins with the estimation of a regional market model, whose beta parameters depend on predetermined information variables. Those parameters support the calculation of time‑varying Treynor ratios used on a comparative performance analysis. A Vector Auto Regressive Model (VAR) is used to estimate the performance causality within this group of markets. The VAR model results provide evidence that there is reciprocal performance across the majority of the selected stock markets.

Traffic and Network Performance Monitoring for Effective Quality of Service and Network Management

2010 ◽  
pp. 201-227
Author(s):  
P. Papantoni-Kazakos ◽  
A. T. Burrell
Keyword(s):  
Mobile Networks ◽  
Network Performance ◽  
Performance Monitoring ◽  
Sequential Algorithm ◽  
Heterogeneous Environments ◽  
Time Varying ◽  
Distributed Environment ◽  
Data Flows ◽  
Network Performance Monitoring ◽  
And Performance

The authors consider distributed mobile networks carrying time-varying heterogeneous traffics. To deal effectively with the mobile and time-varying distributed environment, the deployment of traffic and network performance monitoring techniques is necessary for the identification of traffic changes, network failures, and also for the facilitation of protocol adaptations and topological modifications. Concurrently, the heterogeneous traffic environment necessitates the deployment of hybrid information transport techniques. This chapter discusses the design, analysis, and evaluation of distributed and dynamic techniques which manage the traffic and monitor the network performance effectively, while capturing the dynamics inherent in the mobile heterogeneous environments. Specifically, the design of a monitoring sub-network is sought, where the arising research tasks include: • the adoption of a core sequential algorithm which monitors both the variations in the rates of the information data flows and the dynamics of the network performance. • The identification of the specific operational and performance characteristics of the monitoring systems, when the core algorithm is implemented in a distributed environment; for a given network topology, it is important to identify the minimum size monitoring sub-network for complete “visibility” of data flows and network functions. • Dynamically changing monitoring sub-network architectures, as functions of time-varying network topologies. • The deployment of Artificial Intelligence learning techniques, in the presence of dynamically changing network and information flow environments, to appropriately adapt crucial operational parameters of the data monitoring algorithms.

A Combined Method for Time-Varying Parameter Identification Based on Variational Mode Decomposition and Generalized Morse Wavelet

2020 ◽  
Vol 20 (07) ◽  
pp. 2050077
Author(s):  
Chao Wang ◽  
Jing Zhang ◽  
Hong Pin Zhu
Keyword(s):  
Parameter Identification ◽  
Structural Response ◽  
Time Varying ◽  
Variational Mode Decomposition ◽  
Combined Method ◽  
Moving Vehicle ◽  
Mode Decomposition ◽  
Time Varying Parameter ◽  
Time Varying Systems ◽  
And Performance

Time-varying parameter identification is essential for structural health monitoring and performance evaluation. In this paper, a combined method based on the variational mode decomposition and generalized Morse wavelet is proposed to identify the structural time-varying parameters. Based on the sparse property of structural response signals in wavelet domain, a fast iterative shrinkage-thresholding algorithm is adopted to reduce the noise. Then the de-noised signal is decomposed into multi- modes by the variational mode decomposition, and the generalized Morse wavelet is performed to identify the instantaneous frequency. To validate the proposed method, a numerical example including different frequency variations is studied. Experimental validations of a moving vehicle across a bridge and a time-varying cable system considering two patterns of cable tension variations in the laboratory are carried out to investigate the capability of the proposed approach. It is confirmed that the proposed approach can effectively perform the signal decomposition, while identifying the instantaneous frequencies of the time-varying systems accurately.

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